public static void main(String[] args) {
Logging.stopConsoleLogging();
NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
BasicNetwork network = EncogUtility.simpleFeedForward(2, 4, 0, 1, false);
ResilientPropagation train = new ResilientPropagation(network, trainingSet);
train.addStrategy(new RequiredImprovementStrategy(5));
System.out.println("Perform initial train.");
EncogUtility.trainToError(train, network, trainingSet, 0.01);
TrainingContinuation cont = train.pause();
System.out.println(
Arrays.toString((double[]) cont.getContents().get(ResilientPropagation.LAST_GRADIENTS)));
System.out.println(
Arrays.toString((double[]) cont.getContents().get(ResilientPropagation.UPDATE_VALUES)));
try {
SerializeObject.save("resume.ser", cont);
cont = (TrainingContinuation) SerializeObject.load("resume.ser");
} catch (Exception ex) {
ex.printStackTrace();
}
System.out.println(
"Now trying a second train, with continue from the first. Should stop after one iteration");
ResilientPropagation train2 = new ResilientPropagation(network, trainingSet);
train2.resume(cont);
EncogUtility.trainToError(train2, network, trainingSet, 0.01);
}
/**
* Train the method, to a specific error, send the output to the console.
*
* @param method The method to train.
* @param dataSet The training set to use.
* @param error The error level to train to.
*/
public static void trainToError(
final MLMethod method, final MLDataSet dataSet, final double error) {
MLTrain train;
if (method instanceof SVM) {
train = new SVMTrain((SVM) method, dataSet);
} else {
train = new ResilientPropagation((ContainsFlat) method, dataSet);
}
EncogUtility.trainToError(train, error);
}